Deriving tree growth models from stand models based on the self-thinning rule of Chinese fir plantations

iForest: Biogeosciences and Forestry

article

Xiongqing Zhang¹  Quang V Cao³  Yancheng Qu¹  Jianguo Zhang¹ 

[1] Key Laboratory of Tree Breeding and Cultivation of the National Forestry and Grassland Administration, Research Institute of Forestry;Collaborative Innovation Center of Sustainable Forestry in Southern China, Nanjing Forestry University;School of Renewable Natural Resources, Louisiana State University

关键词: Chinese Fir;    Self-thinning Rule;    Disaggregation;    Stand Model;    Tree Model;   

DOI  :  10.3832/ifor3792-014

学科分类：社会科学、人文和艺术（综合）

来源: Societa Italiana di Selvicoltura ed Ecologia Forestale (S I S E F)

【 摘 要 】

Self-thinning due to density-dependent mortality usually occurs during the forest development. To improve predictions of such processes during forest successions under climate change, reliable stand-level models are needed. In this study, we developed an integrated system of tree- and stand-level models by deriving tree diameter and survival models from stand growth and survival models based on climate-sensitive self-thinning rule of Chinese fir plantations in subtropical China. The resulting integrated system, having a unified mathematical structure, should provide consistent estimates at both tree and stand levels. Predictions were reasonable at both stand and tree levels. Because stand-level values aggregated from the tree model outputs are different from those predicted directly from the stand models, the disaggregation approach was applied to provide numerical consistency between models of different resolutions. Compared to the unadjusted approach, predictions from the disaggregation approach were slightly worse for tree survival but slightly better for tree diameter. Because the stand models were developed under the climate-sensitive self-thinning trajectory, the integrated system could offer reasonable predictions that could aid in managing Chinese fir plantations under climate change.

【 授权许可】

Unknown